⚪️ AI x Product Strategy
Why the future of product isn’t about shipping faster, it’s about learning smarter.
In 2023, Notion shipped a button called “Ask AI.” It promised magical writing help. A year later, they quietly rebuilt the entire user experience around AI-powered workflows custom prompts, team-level automation, and memory. The shift wasn’t about adding AI. It was about redefining the product around how people work.
When Snapchat rolled out its new AI chatbot “My AI” to all users, it probably didn’t expect a revolt. Within a week, Snapchat’s average App Store rating plummeted from 3.05 to 1.67, with 75% of reviews blasting it with one-star critiques . Users were livid about an AI “friend” they never asked for, pinned to the top of their chat feed with no opt-out. The lesson? Simply slapping on an AI feature for the hype can backfire spectacularly if it doesn’t align with what users actually want.
Compare that to Spotify. This year, Spotify quietly wove AI into the core listening experience from an AI DJ that talks through song selections to generative playlists that curate music for each user. The result: CEO Daniel Ek said these AI-powered features are boosting engagement and even lowering churn . In other words, when AI genuinely enhances the product, users stick around. Two companies, two very different outcomes. AI is everywhere in product strategy discussions today, but turning that hype into real product advantage comes down to how you use it.
We’re no longer in the “AI feature” phase. We’re in the product rethinking era. And the winners won’t be the ones who ship the most AI but the ones who turn AI into product leverage.
What Readers Will Get From This Issue
This issue unpacks a growing shift in product strategy:
How leading teams are using AI not as a feature, but as a strategic layer reshaping core value, workflows, and differentiation.
You’ll learn:
The one mindset shift smart teams are making
A real story of how AI reshaped a B2B product’s roadmap
A new lens to spot AI hype vs. AI leverage
A product pick worth studying
Tools, polls, and bonus tips at the bottom
Deep Dive
The age-old mantra in product teams has been to “ship fast and iterate.” But when it comes to AI-driven products, shipping a feature is just the beginning. The real value emerges over time, as the product learns and improves from data and user interactions. In other words, we need to shift from a focus on shipping static features to cultivating continuous learning experiences for users.
Why this shift? Traditional software features are largely deterministic you build a feature, and it behaves the same until you update the code. AI-powered features, by contrast, can evolve with usage. They’re probabilistic and data-driven; their performance can improve (or degrade) as they get more data. This means an AI feature isn’t one-and-done at launch. It requires ongoing tuning, data collection, and iteration in response to real-world use. As product strategist Bryce York puts it, you have to “treat AI features as living, learning components” and improve them continuously with user feedback and data . The faster your product learns and adapts, the more value it delivers. In fact, a new competitive yardstick is emerging: the rate of learning. Your goal is to learn faster about your users’ needs and preferences than your competitors do. Those learnings, in turn, translate into a better product, quicker.
This shift changes how product decisions are made. Instead of asking “What features can we ship next quarter?”, teams are asking “What data loops can we build so the product gets smarter after launch?” For example, TikTok’s meteoric rise can be credited to this mindset. TikTok didn’t win on a novel content format – it won on a superior learning loop. Its AI-powered “For You” feed figures out a new user’s tastes in a matter of minutes, sometimes identifying a user’s interest in as few as 40 minutes of watching content . Every swipe and pause is a data point, and TikTok’s algorithm rapidly adapts the experience to glue your eyes to the screen. It’s a living, learning feature at massive scale.
From Shipping AI Features To Reimagining Product Advantage
Most teams still think in shipping terms.
“How do we add AI?”
“What’s our ChatGPT integration?”
“Can we build a copilot?”
But the real question is:
What would our product do differently if it understood users, data, and context in real time?
AI in product strategy isn’t about a one-time feature release that wows people for a week and then goes stale. It’s about creating a product that keeps getting better the more it’s used. By shifting from a feature factory mindset to a learning-focused mindset, you ensure that AI actually adds cumulative value. Your product becomes smarter, more personalized, more indispensable with each interaction which is a real competitive moat. It’s a shift from delivering code to delivering continuous improvement. Companies that embrace this that build products as dynamic learning experiences will leave those treating AI as a gimmick in the dust.
That I believe is the shift.
Products like Figma, Superhuman, Descript, and Airtable aren’t just adding AI they’re using it to collapse friction, speed decision-making, and learn from user behavior.
Why it matters:
AI becomes part of the value loop learning and adapting from usage
It shifts your roadmap from outputs to intelligent outcomes
It changes how you define success: not just usage, but usefulness
“Product strategy is no longer what features to build. It’s what intelligence to unlock.”
Real Use Case or Story
Use Case 1: Spotify’s AI-Powered Experience
Spotify’s AI journey didn’t start with a headline it started with Discovery Weekly. But over the past 12 months, they’ve gone much deeper.
They launched:
AI DJ: a voice-based music guide that introduces tracks like a live radio host, adjusting based on your history and skips.
AI Playlists: create a personalized playlist from just a few prompts.
Daylist: evolves with your mood and behavior throughout the day.
CEO Daniel Ek said these features are now lowering churn and boosting engagement.
Why does it work?
The features don’t just show intelligence—they deliver real-time adaptation.
They’re fun, but grounded in solving real user problems: finding great music with less effort.
They keep learning. More listening = better personalization.
Compare this to Snapchat’s “My AI” chatbot, which users couldn’t remove and didn’t ask for. The difference? One added friction. The other added value.
Use Case 2: How Grammarly Used AI to Reinvent Its Core Value
Grammarly was already a household name in writing assistance. Then came generative AI. Instead of slapping on a “Rewrite” button, they paused and asked:
“What if Grammarly could think like a writing partner?”
They launched GrammarlyGO, a generative system trained on writing context: your tone, past edits, and goals. It doesn’t just write it co-writes, edits proactively, and adapts to your intent.
Key product moves:
Embedded it in flow: suggestions now surface mid-draft, not after
Tuned it to users: formal vs casual, team vs solo, confident vs curious
Made it learn: the system improves based on real-time corrections
Outcome: Grammarly didn’t just retain its position it made AI table stakes for anyone in the space.
Lesson: Product-led AI is not about adding capability. It’s about reframing the core job your product solves with intelligence.
Intelligent Product of the Week
Product: Dualingo: Duolingo Max, launched in partnership with OpenAI’s GPT-4, brings intelligence into language learning—not by automating lessons, but by creating a real-time tutor experience.
What makes it intelligent:
Roleplay: Chat with AI characters in real-world scenarios (like ordering food in Spanish).
Explain My Answer: Ask the AI to explain your mistakes and teach you the correct form.
Why it matters:
Most language apps can’t provide live feedback or personalized coaching. Duolingo now can at scale.
It’s expanding what language apps can do, not just improving the UI.
Product: Rewind.ai
What it is: A desktop memory tool that records everything you see, say, or hear and lets you search it with natural language.
Why it’s intelligent: It builds a personalized, private knowledge layer on top of your digital life. Unlike typical productivity tools, it learns from your context continuously.
My PoV: Rewind quietly points to the future of apps not search-driven, but context-aware. Imagine tools that know what you’ve seen, written, read, or asked without needing to ask again.
Pulse Polls
What product do you think uses AI well not just as a feature, but as strategy?
→ Reply with your favorite. I’ll feature a few next issue.What’s the wrong way to apply AI in product?
→ When do you feel it gets in the way?
Signals from the Industry
Gartner: 61% of product managers are already using AI or machine learning in their work – AI is quickly becoming a standard part of the product toolbox, not just a niche experiment.
ustwo’s CEO, Nicki Sprinz: “AI is a tool, not a product…it’s a means to an end. If we treat AI as the end, we’re not chasing a customer problem – we’re chasing a tech” . (A great reminder that successful AI products start with the user, not the technology.)
🧵 What makes AI actually useful in products? by Swyx – A thread breaking down when AI feels native vs. bolted-on.
📄 The Generative UI Shift from a16z – Framework for designing around new user interfaces powered by AI.
🎙️ Ben Thompson on “Copilot as Strategy” – Why Microsoft’s real AI bet is product workflow dominance, not just features.
One Thought to Leave
“The best AI products won’t say they’re AI products. They’ll just feel like magic.”
In the era of intelligent products, the winners won’t be those who ship the most features but those who learn the fastest. Every interaction your users have is a chance to make your product smarter and more invaluable. Don’t let that go to waste.
What’s your favorite intelligent product or use case? Reply and I’ll could feature it.
Respond to this week’s pulse check.
Forward this to someone building with data, AI, or product in mind.